We initially assumed that including the set upconv layer would have not been needed.
Intuitively, as in a classical convolutional scheme, the regression task we try to solve can be solved with direct dimensionality reduction, from sensory data to parameters describing uncertainty [1]}, [2]}.
Nonetheless, retaining upsampling layer produced better results in terms of predicted uncertainty and mean.
We associate this behavior with the architecture using skip connections to use the same features extracted from the same point sets originally used ICP.
This creates a 1:1 matching between the data used by the geometric estimator and the network, leaving less room for outliers.
Our network parameterization, with sampling rates and radius, can be found in Table REF .
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